The goal of this project is to develop a dual-mode PET/x-ray mammography scanner (PET/X) to improve the way in which breast cancer therapies are matched to patients by providing early evaluation of therapy efficacy through 'window' studies on an individual patient basis. More than 200,000 women in the US start therapy for breast cancer each year. In 2012 approximately 40,000 deaths will result from breast cancer. Often the first-line chemotherapies, hormonal therapies, and targeted therapies are not effective, requiring second- or third-line therapies. Unfortunately, the success of therapy is usually determined by waiting to see if there is a substantial reduction in tumor size, which may not occur until late in the course of treatment. Early assessment of treatment effectiveness will improve outcomes, reduce morbidity, and reduce cost. A compact PET imaging module that can be used as an add-on with X-ray mammography scanners will help inform the physician's choices of effective therapies for breast cancer patients. Early evaluation of a therapy's effectiveness will help the treating physician individualize a patient's treatment: A baseline (pre treatment) PET image will be taken, then, after a short regimen of a targeted therapy, a post-therapy PET scan will be used to evaluate responses to treatment, and thus will be used to guide selection of post-surgery adjuvant therapy during the window of opportunity between diagnosis and surgery. For this treatment paradigm to become broadly accepted and widely used, the scanner needs to be higher resolution, more compact, and less expensive than standard whole-body PET scanners. In addition a high level of quantitative accuracy is needed. This Phase-I application will show proof of principle of a detector multiplexing technology that substantially reduces costs for detector electronics for a compact high-resolution PET scanner suitable for breast cancer imaging. By using optimal multiplexing technologies for the detector readout electronics, the number of signal channels can be dramatically reduced, thus reducing cost, while still meeting quantitative imaging performance goals.